In this session, we are particularly interested in new methodologies, algorithms, and applications of machine learning approaches to address various challenges in geospatial data science. Major topics may include but are not limited to:
1. Innovative machine learning algorithms for geospatial modeling and simulation;
2. Novel deep learning architectures for geospatial data science, such as ResNet and DenseNet;
3. Exploration of machine learning algorithms in Smart City research;
4. Natural language processing for text-based geospatial information;
5. Computer vision methods for remote sensing image analysis, including land cover classification, change detection, image fusion, and geo-registration;
6. Using machine learning or deep learning for geospatial data collection, especially with UAV and IoT;
7. Transfer learning for geospatial data mining and interpretation;
8. The integration strategies of machine learning and geospatial cyberinfrastructure.
To present a paper in the session, please (I) register and submit your abstract through AAG, and (II) send your PIN, paper title, author list, and abstract to the co-organizers by October 25, 2018 or the extended deadline.
Jin Xing (firstname.lastname@example.org)
Dandong Yin (email@example.com)
Shaowen Wang (firstname.lastname@example.org)
The rapid growth of geospatial data has far exceeded our previous capability of analytics. In addition to new geospatial computing technologies, GIScientists continuously explore new methods to shift geospatial data analytics towards an automatic model building. Recent advancement in deep learning algorithms has proven its success in automatically learning the representative and discriminative features in a hierarchical manner from geospatial big data. For example, high accuracy of land cover classification map has been generated using various convolutional neural networks with remote sensing imagery datasets. However, geospatial data science poses unique challenges in machine learning, such as multitemporal data pre-processing, large-scale network analysis, spatial optimization with scale heterogeneity, and location inference from a large text corpus, to name a few here.
|Presenter||Jin Xing*, Newcastle University, Philip James, Newcastle University , Stuart Barr, Newcastle University , Employing Deep Learning for Real-Time Sewage Level Prediction within Smart Cities||20||3:05 PM|
|Presenter||Preeti Rao*, SEAS, University of Michigan, Meha Jain, SEAS, University of Michigan, Machine learning algorithms for identifying crop types in smallholder farms||20||3:25 PM|
|Presenter||Wonho Jo*, Seoul National University, Deep Learning Based Spatio-temporal Prediction Using LSTM Nerworks||20||3:45 PM|
|Presenter||Cheng-Zhi Qin*, Institute of Geographic Sciences & Natural Resources Research, CAS, Yan-Wen WANG, State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Wei-Ming Cheng, State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, A-Xing Zhu, University of Wisconsin-Madison, Automatic crater detection based on random forest, existing crater map, and spatial structural information from DEM||20||4:05 PM|
|Presenter||Guofeng Cao*, Texas Tech University, A Deep Learning-Based Geostatistical Framework for Geospatial Data Analysis and Modeling||20||4:25 PM|
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